code: def lower_triangular_to_cholesky(tensor_elements):
      """Performs Cholesky decomposition of the diffusion tensor
  
      Parameters
      ----------
      tensor_elements : array (6,)
          Array containing the six elements of diffusion tensor's lower
          triangular.
  
      Returns
      -------
      cholesky_elements : array (6,)
          Array containing the six Cholesky's decomposition elements
          (R0, R1, R2, R3, R4, R5) [1]_.
  
      References
      ----------
      .. [1] Koay, C.G., Carew, J.D., Alexander, A.L., Basser, P.J.,
             Meyerand, M.E., 2006. Investigation of anomalous estimates of
             tensor-derived quantities in diffusion tensor imaging. Magnetic
             Resonance in Medicine, 55(4), 930-936. doi:10.1002/mrm.20832
      """
      R0 = np.sqrt(tensor_elements[0])
      R3 = tensor_elements[1] / R0
      R1 = np.sqrt(tensor_elements[2] - R3**2)
      R5 = tensor_elements[3] / R0
      R4 = (tensor_elements[4] - R3 * R5) / R1
      R2 = np.sqrt(tensor_elements[5] - R4**2 - R5**2)
  
      return np.array([R0, R1, R2, R3, R4, R5])
  
imports: import warnings
  import numpy
  import scipy.optimize
  from dipy.reconst.base import ReconstModel
  from dipy.reconst.dti import TensorFit, design_matrix, decompose_tensor, _decompose_tensor_nan, from_lower_triangular, lower_triangular
  from dipy.reconst.dki import _positive_evals
  from dipy.reconst.vec_val_sum import vec_val_vect
  from dipy.core.ndindex import ndindex
  from dipy.core.gradients import check_multi_b
  from dipy.reconst.multi_voxel import multi_voxel_fit
label_desc: Use image node_id 8 for calling a global function with example usage: lower_triangular_to_cholesky(tensor_elements) and returns: np